Techniques for visualizing probabilistic data generated when designing mechanical assemblies

ABSTRACT

A design engine implements a probabilistic approach to generating designs that exposes automatically-generated design knowledge to the user during operation. The design engine interactively generates successive populations of designs based on a problem definition associated with a design problem and/or a previously-generated population of designs. During the above design process, the design engine generates a design knowledge graphical user interface (GUI) that graphically exposes various types of design knowledge to the user. In particular, the design engine generates a design variable dependency GUI that visualizes various dependencies between designs variables. The design engine also generates a design evolution GUI that animates the evolution of designs across the successive design populations. Additionally, the design engine generates a design exploration GUI that facilitates the user exploring various statistical properties of automatically-generated designs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Patent Application titled,“Techniques for Visualizing Probabilistic Data Generated When DesigningMechanical Assemblies,” filed Jun. 6, 2019 and having Ser. No.16/434,082, which claims the priority benefit of United StatesProvisional Patent Application titled, “Visualizing and DisplayingProbabilistic Knowledge Obtained During Generative Design of MechanicalAssemblies,” filed on Apr. 22, 2019 and having Ser. No. 62/837,157, andalso claims the priority benefit of United States provisional patentapplication titled, “Visualizing And Displaying Probabilistic KnowledgeObtained During Generative Design of Mechanical Assemblies”, filed May1, 2019 and having Ser. No. 62/841,767. The subject matter of theserelated applications is hereby incorporated herein by reference.

BACKGROUND Field of the Various Embodiments

The various embodiments relate generally to computer-aided designtechnology and, more specifically, to techniques for visualizingprobabilistic data generated when designing mechanical assemblies.

Description of the Related Art

In a typical mechanical engineering and design workflow, a designer usesa computer-aided design (CAD) application to generate CAD models thatrepresent mechanical components. The designer also can use the CADapplication to combine two or more CAD models to generate a CADassembly. The CAD models included in the CAD assembly are typicallycoupled together in a manner that achieves a particular function toaddress a specific design problem. For example, a CAD assembly thatrepresents an automobile transmission could include a collection of CADmodels representing gears that are coupled together to provide torqueconversions. The design problem addressed in this example would be theneed to transmit torque from the automobile crankshaft to the wheels ofthe automobile.

Generating a CAD assembly using a CAD application is typically amanually-performed, multi-step process. Initially, a designer formulatesthe design problem to be addressed by the CAD assembly by determining aset of design objectives the CAD assembly should meet. For example, whenformulating the automobile transmission design problem discussed above,the designer could determine that the transmission should implement aspecific conversion ratio in order to convert an input torque receivedfrom the automobile crankshaft to an output torque that is applied tothe wheels of the automobile. In conjunction with determining the set ofdesign objectives, the designer usually further defines the designproblem by determining a set of design constraints that the CAD assemblyshould not violate. In the transmission design problem discussed above,for example, the designer could determine that the mass of thetransmission should not exceed a particular value. Once the designerdetermines the various design objectives and design constraints, thedesigner can then use the CAD application to generate the CAD assemblyby manually generating and combining various CAD models. For example,the transmission designer could determine a specific arrangement of CADmodels representing a selected set of gears to generate a CAD assemblythat implements the desired conversion between input and output torques.

As previously mentioned, through the above design process, the designergenerates a CAD assembly that is meant to address a particular designproblem. Once generated, the designer can further test the CAD assembly,via computer simulation, to determine whether the various designobjectives are met without violating the different constraints. Thedesign process is usually repeated iteratively, in a trial-and-errorfashion, in an attempt to explore the overall design space associatedwith the particular design problem and produce one or more successfuldesigns.

Conventional design processes, like the design process set forth above,can be automated to a certain extent using various algorithmictechniques. For example, generative design techniques can be implementedto automatically generate CAD assemblies that meet a set of stateddesign objectives without violating a set of enumerated designconstraints. Alternatively, constraint-based programming techniques canbe implemented to identify feasible CAD assemblies that address stateddesign objectives to varying degrees. Such algorithmic techniques reducethe number of manual operations that have to be performed by thedesigner, where the algorithms automatically perform those operationsinstead, thereby streamlining the overall design process. When thedesign process is complete, the designer is presented with a handful ofautomatically-generated designs that can be inspected using aconventional CAD application graphical user interface (GUI).

One drawback of the above approach is that inspecting theautomatically-generated designs resulting from the above approach usinga conventional CAD application GUI does not provide designers withappropriate levels of intuitive design knowledge and understanding.Consequently, algorithmic design techniques in general, and conventionalCAD application GUIs in particular, do not empower designers to makeinformed decisions about how to modify, or generate alternatives to,algorithmically-generated designs. Further, without being able todevelop an intuitive understanding of how successful designs areconstructed and structured, designers cannot communicate designknowledge to other designers. Consequently, designers can havedifficulty collaborating with one another on design projects.

As the foregoing illustrates, what is needed in the art are moreeffective ways to automatically generate designs of mechanicalassemblies.

SUMMARY

Various embodiments include a computer-implemented method for generatingdesigns, including generating a first plurality of designs based on aproblem definition associated with a design problem and a first set ofdesign variables associated with the design problem, analyzing the firstplurality of designs statistically to determine a first statisticalattribute associated with a first design variable included in the firstset of design variables, and generating a graphical user interface (GUI)based on the first statistical attribute and the first plurality ofdesigns to graphically depict the first statistical attribute.

At least one technological advantage of the disclosed techniquesrelative to the prior art is that design knowledge is exposed to theuser via the GUI during automatic generation of designs. This exposurefacilities the user in developing an intuitive understanding of howsuccessful designs are constructed and structured. As a result, the useris empowered to make informed decisions regarding how to modify designsand/or how to explore alternative design options.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the variousembodiments can be understood in detail, a more particular descriptionof the inventive concepts, briefly summarized above, may be had byreference to various embodiments, some of which are illustrated in theappended drawings. It is to be noted, however, that the appendeddrawings illustrate only typical embodiments of the inventive conceptsand are therefore not to be considered limiting of scope in any way, andthat there are other equally effective embodiments.

FIG. 1 illustrates a system configured to implement one or more aspectsof the present embodiments;

FIG. 2A is an exemplary illustration of the design problem of FIG. 1,according to various embodiments;

FIG. 2B is an exemplary illustration of one of the design options ofFIG. 1, according to various embodiments;

FIG. 3 is a more detailed illustration of the design engine of FIG. 1,according to various embodiments;

FIG. 4 is an exemplary illustration of the design variable dependencygraphical user interface of FIG. 3, according to various embodiments;

FIG. 5 is a flow diagram of method steps for visualizing design variabledependencies, according to various other embodiments;

FIG. 6A-6D set forth exemplary illustrations of the design evolutiongraphical user interface of FIG. 3, according to various embodiments;

FIG. 7 is a flow diagram of method steps for visualizing the evolutionof a design population, according to various other embodiments;

FIG. 8A-8B set forth exemplary illustrations of the design explorationgraphical user interface of FIG. 3, according to various embodiments;

FIG. 9 is a flow diagram of method steps for visualizing one or moreaspects of a design, according to various embodiments.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a more thorough understanding of the various embodiments.However, it will be apparent to one skilled in the art that theinventive concepts may be practiced without one or more of thesespecific details.

As noted above, conventional algorithmic techniques for generating CADassemblies operate automatically and therefore obfuscate the designprocess from a user. The user thus has a reduced ability to developdesign knowledge that reflects an intuitive understanding of howsuccessful designs are structured. Conventional CAD application GUIs donot expose any such design knowledge to the user and simply present theuser with automatically-generated CAD assemblies for inspection. Withoutan effective way to develop this design knowledge, the user may not beable to effectively make informed decisions regarding how to modifydesigns and/or how to explore alternative designs and may not be able tocommunicate effectively with others during collaborative design.

To address these issues, a design engine implements a probabilisticapproach to generating designs that exposes automatically-generateddesign knowledge to the user during operation via one or more graphicaluser interfaces. The design engine initially generates a population ofdesigns based on a problem definition associated with a design problem.The design engine simulates the performance of each design and thenselects the most performant designs. By analyzing the most performantdesigns, the design engine identifies design variables that aredependent on one another. The design engine then generates a probabilitymodel indicating conditional probabilities between design valuesassociated with dependent design variables. The design engineiteratively samples the probability model to generate a subsequentpopulation of designs. The design engine performs this design processrepeatedly to generate successive populations of designs.

In conjunction with the above design process, the design enginegenerates a design knowledge GUI that graphically exposes various typesof design knowledge to the user. In particular, the design enginegenerates a design variable dependency GUI that visualizes variousdependencies between designs variables. The design engine also generatesa design evolution GUI that animates the evolution of designs acrosssuccessive design populations. Additionally, the design engine generatesa design exploration GUI that facilitates the user exploring variousstatistical properties of automatically-generated designs.

At least one technological advantage of the disclosed techniquesrelative to the prior art is that the design engine exposes designknowledge to the user via the design knowledge GUI when automaticallygenerating designs rather than obfuscating this design knowledge fromthe user, which occurs with conventional CAD application GUIs. Thisexposure facilities the user in developing an intuitive understanding ofhow successful designs are constructed and structured. As a result, thedesign knowledge GUI empowers the user to make informed decisionsregarding how to modify designs and/or how to explore alternative designoptions in a manner that is not possible with conventional CADapplication GUIs. Based on the design knowledge provided by the designengine, the user can more easily communicate with others about variousaspects and properties of automatically-generated designs, therebyfacilitating enhanced collaboration between users. The disclosedtechniques can thus be applied to automatically generate numerousdesigns for mechanical assemblies in a streamlined manner, withoutpreventing the user from obtaining useful design knowledge andunderstanding, as is typical with prior art algorithmic techniques andGUIs. These technological advantages represent one or more technologicaladvancements over prior art approaches.

System Overview

FIG. 1 illustrates a system configured to implement one or more aspectsof the present invention. As shown, a system 100 includes one or moreclients 110 and one or more servers 130 coupled together via a network150. A given client 110 or a given server 130 may be any technicallyfeasible type of computer system, including a desktop computer, a laptopcomputer, a mobile device, a virtualized instance of a computing device,a distributed and/or cloud-based computer system, and so forth. Network150 may be any technically feasible set of interconnected communicationlinks, including a local area network (LAN), wide area network (WAN),the World Wide Web, or the Internet, among others.

As further shown, a client 110 includes a processor 112, input/output(I/O) devices 114, and a memory 116, coupled together. Processor 112includes any technically feasible set of hardware units configured toprocess data and execute software applications. For example, processor112 could include one or more central processing units (CPUs). I/Odevices 114 include any technically feasible set of devices configuredto perform input and/or output operations, including, for example, adisplay device, a keyboard, and a touchscreen, among others.

Memory 116 includes any technically feasible storage media configured tostore data and software applications, such as, for example, a hard disk,a random-access memory (RAM) module, and a read-only memory (ROM).Memory 116 includes a database 118 and a design engine 120(0). Designengine 120(0) is a software application that, when executed by processor112, interoperates with a corresponding software application executingon server 130, as described in greater detail below.

Server 130 includes a processor 132, I/O devices 134, and a memory 136,coupled together. Processor 132 includes any technically feasible set ofhardware units configured to process data and execute softwareapplications, such as one or more CPUs. I/O devices 134 include anytechnically feasible set of devices configured to perform input and/oroutput operations, such as a display device, a keyboard, or atouchscreen, among others.

Memory 136 includes any technically feasible storage media configured tostore data and software applications, such as, for example, a hard disk,a RAM module, and a ROM. Memory 136 includes a database 118(0) and adesign engine 120(1). Design engine 120(1) is a software applicationthat, when executed by processor 132, interoperates with design engine120(0).

As a general matter, database 118(0) and 118(1) represent separateportions of a distributed storage entity. Thus, for simplicity,databases 118(0) and 118(1) are collectively referred to hereinafter asdatabase 118. Similarly, design engine 120(0) and design engine 120(1)represent separate portions of a distributed software entity that isconfigured to perform any and all of the inventive operations describedherein. Thus, for simplicity, design engines 120(0) and 120(1) arecollectively referred to hereinafter as design engine 120.

In operation, design engine 120 is configured to generate a problemdefinition 122 based on interactions with a user. Problem definition 122includes various data that defines, at least in part, an engineeringproblem to be addressed by a mechanical assembly. For example, problemdefinition 122 could include geometry associated with the engineeringproblem, a set of design variables associated with the engineeringproblem, a set of design objectives associated with the engineeringproblem, and/or a set of design constraints associated with theengineering problem, among others. An exemplary problem definition isdescribed below in conjunction with FIG. 2A. Based on problem definition122, design engine 120 generates one or more design options 124. A givendesign option 124 defines a CAD assembly that addresses, at least tosome degree, the engineering problem defined via problem definition 122.Design engine 120 also generates design data 126. Design data 126includes various statistical information design engine 120 generatesduring automatic generation of design options 124. Based on design data126, design engine 120 generates a design knowledge GUI 128 that can bedisplayed to the user via a display device. Design knowledge GUI 128includes three different GUIs that convey the above-mentionedstatistical information to the user. These three GUIs are described ingreater detail below in conjunction with FIG. 3 and in conjunction withFIGS. 4-5, 6-7, and 8-9, respectively.

Exemplary Problem Definition and Design Option

FIG. 2A is an example of the design problem of FIG. 1, according tovarious embodiments. As shown, design problem 122 includes anenvironmental object 1 (EO1), an environmental object 2 (E02), joints J1through J8, design variables X1 through X5, and design objective 200.EO1 and EO2 represent pre-existing 3D geometry associated with theengineering problem. In the example shown, EO1 could be the chassis ofan automobile, while EO2 could be a wheel of the automobile that needsto be coupled to the automobile chassis by a mechanical assembly of somesort.

Joints J1 through J8 represent specific points to which components canbe coupled to generate CAD assemblies. Those components could includebeams, springs, dampers, and so forth. A given joint can be assigned aspecific joint type, although a specific assignment of joint type can beomitted. Joints J1 through J8 are initially positioned by the user viainteractions with design engine 120. The user can also define, viainteractions with design engine 120, one or more design objectives, suchas design objective 200. Design objective 200 defines a target set ofdynamics that a given CAD assembly should achieve at a location on EO1corresponding to design objective 200. For example, design objective 200could define a target time-varying acceleration that a given CADassembly should achieve at the location on EO1 corresponding to designobjective 200. Design problem 122 can include any number of designobjectives associated with any set of positions. Design problem 122 canalso include one or more design objectives that are not associated withany particular position. For example, a given design objective couldindicate that the total number of components should be minimized.

Design variables X1 through X5 represent exemplary design variables towhich design engine 120 assigns specific values when generating adesign. The value assigned to a given design variable may be referred toherein as a “design value.” In practice, design engine 120 implements adifferent design variable for each different pair of joints, althoughfor clarity only exemplary design variables X1 through X5 are shown.Design engine 120 assigns a given design value to a given designvariable to represent a specific component type that can couple togethertwo joints, as described in greater detail below in conjunction withFIG. 2B.

FIG. 2B is an example of one of the design options of FIG. 1, accordingto various embodiments. As shown, design option 124 includes beam B1coupled between joints J4 and J8, beam B2 coupled between joints J2 andJ7, spring S1 coupled between joints J8 and J6, and spring S2 coupledbetween joints J7 and J5. The various beams and springs shown, and theparticular couplings of those components to specific joints,collectively represent a CAD assembly that may address the engineeringproblem set forth in problem definition 122, at least to some degree.

To generate design option 124, design engine 120 assigns specific valuesto the various design variables associated with problem definition 122to represent specific types of components. For example, design engine120 could assign a value of “1” to design variable X2 to represent beamB1. Design engine 120 could assign a value of “0” to design variable X3to indicate the absence of a component. Design engine 120 could assign avalue of “2” to design variable X5 to represent spring S2. Design engine120 may also assign values to other design variables in order torepresent the positioning and/or joint type associated with joints J1through J8, in some embodiments.

Design engine 120 is configured to implement a statistically-drivendesign process to generate design values for design options 124 inconjunction with generating design data 126, as described in greaterdetail below in conjunction with FIG. 3.

Software Overview

FIG. 3 is a more detailed illustration of the design engine of FIG. 1,according to various embodiments. As shown, design engine 120 includes adesign initializer 300, an assembly modifier 310, a performanceevaluator 320, a dependency analyzer 330, a statistical analyzer 340,and a probabilistic generator 350.

In operation, design initializer 300 generates an initial designpopulation 302 of randomized designs based on problem definition 122. Agiven design included in initial design population 302 includes arandomly selected or randomly omitted component for each pair of jointsset forth in problem definition 122. As mentioned, a given componentcould be, for example, a beam, spring, a damper, and any othertechnically feasible mechanical component. Each design included ininitial design population 302 also includes a randomly selected jointtype for each joint. A given joint type could be, for example, a balljoint, a slider joint, an elbow joint, and so forth. Components andjoints may be collectively referred to herein as “design elements.”

In practice, design initializer 300 generates a given design included ininitial design population 302 by assigning random values to each designvariable associated with the given design, thereby generating a specificset of design elements. For example, design initializer 300 could assigna value of “1” to design variable X2 of FIG. 2A in order to indicatethat the corresponding component should be a beam component or assign aspecific value to another design variable to indicate that acorresponding joint should have a given joint type.

Assembly modifier 310 processes each design included in initial designpopulation 302 in order to identify and repair any infeasible designs.Assembly modifier 310 then outputs feasible designs 312 that include anyinitially feasible designs as well as any repaired designs. As referredto herein, the term “infeasible” is used to describe designs thatviolate certain design rules. For example, a design for an assembly thatincludes one or more components that are not attached to joints at bothends could be considered infeasible, because such “dangling” componentsdo not contribute to the overall functionality of the design. In anotherexample, a design for an assembly that includes too many componentsconnected to the same joint could be considered infeasible because areal-world joint can only handle a maximum number of connections.Assembly modifier 310 identifies infeasible designs and then implementsa constraint-oriented programming approach to modify those designs.

Performance evaluator 320 simulates the dynamic behavior of feasibledesigns 312 to generate performance data that indicates the degree towhich each feasible design 312 meets the various design objectives setforth in problem definition 122. Performance evaluator 320 ranksfeasible designs 312 based on the associated performance data andselects the best-performing designs, which are shown as performantdesigns 322. Performance evaluator 320 may select the top N performingdesigns, N being an integer, or select the top N % designs, N being adecimal, among other approaches to selecting the best-performingdesigns.

Dependency analyzer 330 processes each performant design 322 anddetermines various statistical dependencies between design variables,thereby generating design variable dependencies 332. Design variabledependencies 332 constitute a portion of the statistical informationincluded in design data 126. Dependency analyzer 330 can determine thattwo design variables depend on one another by comparing the expectedprobabilities of those two design variables having specific values tothe observed probabilities of those two design variables having specificvalues. Dependency analyzer 330 can quantify the dependencies betweendesign variables using various statistical approaches.

In one embodiment, dependency analyzer 330 may implement a chi-squaredtest to generate a chi-squared value for a given pair of designvariables, where the chi-squared value quantifies the statisticaldependency between the given pair of design variables. Dependencyanalyzer 330 may then determine that a pair of design variables aredependent on one another when the associated chi-squared value exceeds athreshold. Dependency analyzer 330 may also account for missingobservations when implementing this approach by selecting differentthresholds against which each chi-squared value is compared based on howmany observations are missing.

Based on design variable dependencies 332, dependency analyzer 330generates a design variable dependency GUI 334 that is included indesign knowledge GUI 128. Design variable dependency GUI 334 visuallyquantifies statistical dependencies between various design variables,thereby providing the user with insight into how the various designelements within a successful design relate to one another. Designvariable dependency GUI 334 is described in greater detail below inconjunction with FIGS. 4-5.

Statistical analyzer 340 analyzes performant designs 322 and designvariables dependencies 332 to generate a probability model 342.Probability model 342 constitute another portion of the statisticalinformation included in design data 126. Conceptually, probability model342 indicates various probabilities of certain design elements occurringin designs that are considered high-performing. More specifically,probability model 342 includes a set of conditional probability valuesassociated with each pair of design variables that are determined, viadependency analyzer 330, to be statistically dependent on one another.The set of conditional probabilities includes a different conditionalprobability value for each different combination of design values thatcan be assigned to the pair of design variables. For example, supposedependency analyzer 330 determines that design variables X10 and X11 arestatistically dependent on one another via the approach describedpreviously. Statistical analyzer 340 could generate a set of conditionalprobability values indicating the probability of design variable X10being assigned a value of “2” (indicating, e.g., a beam component),conditioned on the probability of design variable X11 being assigned avalue of “1” (indicating, e.g., a ball joint).

In one embodiment, probability model 342 is a two-dimensional (2D)matrix of values with rows that correspond to “parent” elements andcolumns that correspond to “child” elements. Parent and child elementscorrespond to design variables that are determined to be statisticallydependent on one another. A given cell included in the 2D matrix ofvalues stores a conditional probability value that indicates theprobability of a child design variable being assigned a specific valueconditioned on the probability of the parent design variable beingassigned a specific value.

Based on probability model 342, statistical analyzer 340 generates adesign evolution GUI 344 that is included in design knowledge GUI 128.Design evolution GUI 344 animates how the probabilities of variousdesign elements being included in successful designs changes oversuccessive populations of designs, thereby providing the user withinsight into how the design process unfolds. Design evolution GUI 344 isdescribed in greater detail below in conjunction with FIGS. 6-7.

Probabilistic generator 350 is configured to generate design population352 based on samples extracted from probability model 342. In order togenerate a given design included in design population 352, probabilisticgenerator 350 assigns specific values to the design variables includedin the given design with a probability distribution that is derived fromprobability model 342. In this manner, probabilistic generator 350 cangenerate new populations of designs that have design variable valuesderived from high-performing designs included in previous populations ofdesigns. Probabilistic generator 350 also generates design data 126based on design variable dependencies 332, probability model 342, anddesign population 352.

If certain convergence criteria are met, probabilistic generator 350outputs the newly-generated population of designs as design options 124.Otherwise, probabilistic generator 350 provides design population 352 toassembly modifier 310 and the above-described process repeats. Theconvergence criteria could indicate, for example, a maximum number ofiterations to perform or a maximum amount of time to execute, amongothers.

In one embodiment, probabilistic generator 350 may implement a Gibbssampling technique to sample probability model 342 when generating a newdesign to include in design population 352. In so doing, probabilisticgenerator 350 randomly assigns values to the various design variablesassociated with the design and then updates these values based onprobability values sampled from probability model 342. By repeating thisprocess across one or more iterations, the values assigned to the designvariables may eventually converge to having the probability distributionset forth in probability model 342.

Based on probability model 342 and design population 352, probabilisticgenerator 350 generates a design exploration GUI 354 that is included indesign knowledge GUI 128. Design exploration GUI 354 allows the user tointeract with designs in order to expose various statisticalrelationships between design elements included in those designs, therebyfacilitating user exploration of design knowledge. Design explorationGUI 354 is described in greater detail below in conjunction with FIGS.8-9.

Via the above operations, design engine 120 estimates probabilitydistributions of design variable values associated with high-performingdesigns across increasingly performant design populations and then usesthese probability distributions to generate subsequent designpopulations. In practice, this approach can converge quickly to generatedesign options 124 faster than possible with conventional generativedesign techniques, such as generative design and constraint-orientedprogramming techniques, among others. During this design process, designengine 120 also generates the various GUIs included in design knowledgeGUI 128. These different GUIs expose specific design knowledge to theuser and therefore facilitate the user in developing an intuitiveunderstanding of how successful and high-performing designs arestructured. These different GUIs are described in greater detail belowin conjunction with FIGS. 4-9.

Design Variable Dependency GUI

FIG. 4 is an exemplary illustration of the design variable dependencyGUI of FIG. 3, according to various embodiments. As shown, designvariable dependency GUI 334 includes a perimeter that is divided intosections 400(1) through 400(5). Each one of sections 400(1) through400(5) corresponds to a different design variable X1 through X5,respectively. Portions of some sections 400 are coupled together viachords 402. In particular, a portion of section 400(1) is coupled to aportion of section 400(3) by chord 402(1), a portion of section 400(3)is coupled to a portion of section 400(4) by chord 402(2), and a portionof section 400(4) is coupled to a portion of section 400(1) by chord402(3).

Chords 402 visual represent statistical dependencies between variousdesign variables. For example, chord 402(1) could visually represent astatistical dependency between design variable X1 and design variableX3. Chords 402 may also, in some embodiments, visually representstatistical dependencies between specific design values that can beassigned to those design variables. For example, chord 402(1) couldvisually represent a statistical dependency between design variable X1being assigned a value of “1” (corresponding to a beam element 410) anddesign variable X3 being assigned a value of “2” (corresponding to aspring element 420). Chords 402 can have varying thicknesses torepresent varying strengths of statistical dependencies between designvariables and/or design values. For example, chord 402(1) is thickerthan chord 402(2), indicating that design variables X1 and X5 have agreater statistical dependency on one another than design variables X3and X4.

Via the approach described above, design variable dependency GUI 334conveys design knowledge to the user indicating how successful designscan be structured. Accordingly, design variable dependency GUI 334facilitates the user developing an intuition regarding theinterrelationships between the various design elements included in eachdesign. Dependency analyzer 330 generates design variable dependency GUI334 via the approach described below in conjunction with FIG. 5.

FIG. 5 is a flow diagram of method steps for visualizing design variabledependencies, according to various other embodiments. Although themethod steps are described in conjunction with the systems of FIGS. 1-4,persons skilled in the art will understand that any system configured toperform the method steps in any order falls within the scope of thepresent embodiments.

As shown, a method 500 begins at step 502, where dependency analyzer 330within design engine 120 determines one or more statistical dependenciesbetween design variables based on a distribution of design values withina population of successful designs. Dependency analyzer 330 candetermine that two design variables depend on one another by comparingthe expected probabilities of those two design variable having specificvalues to the observed probabilities of those two design variableshaving specific values. In one embodiment, dependency analyzer 330implements a chi-squared test across design variable values to determinestatistical dependencies between design variables.

At step 504, dependency analyzer 330 generates design variabledependency GUI 334 to depict the distribution of design values acrossthe various design variables associated with problem definition 122.Dependency analyzer 330 arranges the design variables around a circularperimeter and then assigns each design variable to a different sectionof that perimeter. Dependency analyzer 330 may allocate a larger sectionof the perimeter to a design variable with greater statisticaldependencies on other design variables.

At step 506, dependency analyzer 330 generates one or more chords thattraverse design variable dependency GUI 332 and couple together sectionsof the perimeter associated with design variables that are statisticallydependent on one another. A given chord may also indicate a statisticaldependency between specific values that can be assigned to the designvariables. Dependency analyzer 330 generates each chord to have athickness that represents the strength of the statistical dependencybetween design variables and/or design values.

Referring generally to FIGS. 4-5, the techniques described hereinadvantageously can be applied to provide the user with design knowledgegenerated via the probabilistic approach to generating designs describedabove in conjunction with FIG. 3. These techniques therefore facilitatethe user in developing an understanding of how successful designs can bestructured, thereby empowering the user to make informed design changesand to more effectively communicate with others regarding designs.

Design Evolution GUI

FIG. 6A-6D set forth exemplary illustrations of the design evolution GUIof FIG. 3, according to various embodiments. FIG. 6A depicts frame600(A), FIG. 6B depicts frame 600(B), FIG. 6C depicts frame 600(C), andFIG. 6D depicts frame 600(D). Statistical analyzer 340 generates eachframe 600 during a given design iteration in order to visually depictthe distribution of design variable values in the design populationcorresponding to that iteration.

A given frame 600 depicts a set of components that couple together thedifferent joints set forth in problem definition 122. For a givencomponent that couples together a given pair of joints, design evolutionGUI 344 displays the given component with specific visual propertiesthat represent the probability of the component coupling together thegiven pair of joints. In the exemplary frames 600 shown in FIGS. 6A-6D,components are shown as lines with thicknesses that correspond toprobabilities of those components appearing between a corresponding pairof joints. In various embodiments, components may also be shown withdifferent translucency values to represent different probabilitiesand/or different colors to represent specific component types that aremost likely to occur.

Referring now to FIG. 6A, initially, design evolution GUI 344 displaysmany different possible configurations of components that can coupletogether the various joints. The different components are shown withsimilar line thicknesses, indicating that none of the components occurmore frequently in the current design population than any others.Referring now to FIG. 6B, after one design iteration, design evolutionGUI 344 displays fewer possible configurations of components, and somecomponents are more likely to occur in the current design populationthat others. Referring now to FIG. 6C, after another design iteration,design evolution GUI 344 displays even fewer possible configurations ofcomponents, with specific components having a distinctly greaterlikelihood of occurring in the design population that others. Referringnow to FIG. 6D, in the final design iteration, the distribution ofdesign variable values in the final design population converges to ahandful of components that occur with high likelihood. During the aboveprocess, statistical analyzer 340 analyzes the distribution of designvariable values associated with each design population and updatesdesign evolution GUI 344 accordingly.

Because design evolution GUI 344 graphically depicts how the designprocess unfolds over time, design evolution GUI 344 helps the user tounderstand how successive design populations change to arrive at thefinal design population. This approach differs from conventionalalgorithmic approaches to generating designs that obfuscate the designprocess from the user. The techniques implemented by statisticalanalyzer 340 when generating design evolution GUI 344 are described ingreater detail below in conjunction with FIG. 7.

FIG. 7 is a flow diagram of method steps for visualizing the evolutionof a design population, according to various other embodiments. Althoughthe method steps are described in conjunction with the systems of FIGS.1-3 and 6, persons skilled in the art will understand that any systemconfigured to perform the method steps in any order falls within thescope of the present embodiments.

As shown, a method 700 begins at step 702, where statistical analyzer340 within design engine 120 of FIG. 3 generates design evolution GUI344 based on problem definition 122. Design evolution GUI 344graphically depicts various elements of problem definition 122,including any environmental objects defined in problem definition 122and various joints specified in problem definition 122. Based on problemdefinition 122, design engine 120 generates a current population ofdesigns in the manner described above in conjunction with FIG. 3.

At step 704, statistical analyzer 340 analyzes the statisticalproperties of the current population of designs to determine thedistribution of design values assigned to the design variables set forthin problem definition 122. For example, statistical analyzer 340 coulddetermine the frequency with which a specific component type couplestogether a particular set of joints across all designs included in thecurrent design population.

At step 706, statistical analyzer 340 updates design evolution GUI 344to depict statistically probable component placements. In so doing,design evolution GUI 344 displays a given component with visualproperties that represent the probability of the component appearing inthe depicted location. Design evolution GUI 344 can also display a givencomponent with visual properties that indicate the most likely componenttype assigned to the given component.

At step 708, statistical analyzer 340 proceeds to analyzing a subsequentpopulation of designs. The method 700 then returns to step 704 andcontinues iteratively, thereby generating an animation that depicts howthe probability distribution of design variables evolves over successivedesign iterations.

Referring generally to FIGS. 6-7, the disclosed techniques provide theuser with detailed insight into how the design process evolves over manydesign populations. Accordingly, the disclosed techniques can be appliedto help the user develop an intuition regarding how successful designsare generated.

Design Exploration GUI

FIG. 8A-8B set forth exemplary illustrations of the design explorationGUI of FIG. 3, according to various embodiments. As shown, view 800(A)of FIG. 8A and view 800(B) of FIG. 8B depict joints J1 through J8coupled together via various components. Design exploration GUI 354displays a given component within views 800(A) and 800(B) with specificvisual attributes that indicate the statistical dependency of the givencomponent on a component the user selects via a cursor 802.

For example, referring now to FIG. 8A, design exploration GUI 354displays component 810 with an elevated thickness that indicates astatistical dependency on a user-selected component 812. Similarly,referring now to FIG. 8B, design exploration GUI 354 displays component820 with an elevated thickness that indicates a statistical dependencyon a user-selected component 822. In practice, design exploration GUI354 can display the various generic components shown in FIGS. 8A-8B asspecific component types, such as beams, springs, dampers, and so forth.Further, design exploration GUI 354 can display components with avariety of different visual properties to indicate a statisticaldependency on a user-selected component, including different colors,translucencies, and so forth. In one embodiment, the various views 800associated with design exploration GUI 354 can be superimposed over agraphical rendering of one or more mechanical assembly designs.

Probabilistic generator 350 of FIG. 3 generates design exploration GUI354 based on design variable dependencies 332 and design population 352.Via the techniques described above, design exploration GUI 354 helps theuser to explore designs included in design population 352 and understandhow different components of those designs depend on one another. Basedon insights and design knowledge gained via interactions with designexploration GUI 354, the user is empowered to make informed designdecisions. In one embodiment, probabilistic generator 350 may generatedesign exploration GUI 354 based on multiple successive designpopulations. The techniques implemented by probabilistic generator 350when generating design exploration GUI 354 are described in greaterdetail below in conjunction with FIG. 9.

FIG. 9 is a flow diagram of method steps for visualizing one or moreaspects of a design, according to various embodiments. Although themethod steps are described in conjunction with the systems of FIGS. 1-3and 8, persons skilled in the art will understand that any systemconfigured to perform the method steps in any order falls within thescope of the present embodiments.

As shown, a method 900 begins at step 902, where probabilistic generator350 of FIG. 3 generates design explorer GUI 354 based on problemdefinition 122. Design explorer GUI 354 graphically depicts specificgeometry set forth in problem definition 122, including anyenvironmental objects defined in problem definition 122 and variousjoints specified in problem definition 122. Exemplary views of designexplorer GUI 354 are shown in FIGS. 8A-8B.

At step 904, probabilistic generator 350 obtains design variabledependencies 332 generated by dependency analyzer 330. Design variabledependencies 332 indicate statistical dependencies between physicallyrelated design variables. As described above in conjunction with FIG. 3,dependency analyzer 330 can determine various statistical dependenciesbetween design variables and corresponding values by performing achi-squared analysis on the current design population, among otherpossible techniques.

At step 906, probabilistic generator 350 updates design exploration GUI354 based on the current population of designs to visually depict someor all components that can couple together joints in the currentpopulation of designs. For example, probabilistic generator 350 couldproject all possible configurations of components set forth in thecurrent population of designs onto design exploration GUI 354.

At step 908, probabilistic generator 350 modifies the display of one ormore components displayed in design exploration GUI 354 in proportion tocorresponding statistical dependencies on a user-selected component. Forexample, probabilistic generator 350 could analyze design variabledependencies 332 to determine the statistical dependency of each designvariable on the design variable associated with the user-selectedcomponent. Probabilistic generator 350 could then modify thetranslucencies of the components associated with those design variablesrelative to the determined statistical dependencies.

In sum, a design engine implements a probabilistic approach togenerating designs that exposes automatically-generated design knowledgeto the user during operation via one or more graphical user interfaces(GUIs). The design engine initially generates a population of designsbased on a problem definition associated with a design problem. Thedesign engine simulates the performance of each design and then selectsthe most performant designs. By analyzing the most performant designs,the design engine identifies design variables that are dependent on oneanother. The design engine then generates a probability model indicatingconditional probabilities between design values associated withdependent design variables. The design engine iteratively samples theprobability model to generate a subsequent population of designs. Thedesign engine performs this design process repeatedly to generatesuccessive populations of designs.

In conjunction with the above design process, the design enginegenerates a design knowledge GUI that graphically exposes various typesof design knowledge to the user. In particular, the design enginegenerates a design variable dependency GUI that visualizes variousdependencies between designs variables. The design engine also generatesa design evolution GUI that animates the evolution of designs acrosssuccessive design populations. Additionally, the design engine generatesa design exploration GUI that facilitates the user exploring variousstatistical properties of automatically-generated designs.

At least one technological advantage of the disclosed techniquesrelative to the prior art is that the design engine exposes designknowledge to the user via the design knowledge GUI when automaticallygenerating designs rather than obfuscating this design knowledge fromthe user, which occurs with conventional CAD application GUIs. Thisexposure facilities the user in developing an intuitive understanding ofhow successful designs are constructed and structured. As a result, thedesign knowledge GUI empowers the user to make informed decisionsregarding how to modify designs and/or how to explore alternative designoptions in a manner that is not possible with conventional CADapplication GUIs. Based on the design knowledge provided by the designengine, the user can more easily communicate with others about variousaspects and properties of automatically-generated designs, therebyfacilitating enhanced collaboration between users. The disclosedtechniques can thus be applied to automatically generate numerousdesigns for mechanical assemblies in a streamlined manner, withoutpreventing the user from obtaining useful design knowledge andunderstanding, as is typical with prior art algorithmic techniques andGUIs. These technological advantages represent one or more technologicaladvancements over prior art approaches.

1. Some embodiments include a computer-implemented method for generatingdesigns, the method comprising generating a first plurality of designsbased on a problem definition associated with a design problem and afirst set of design variables associated with the design problem,analyzing the first plurality of designs statistically to determine afirst statistical attribute associated with a first design variableincluded in the first set of design variables, and generating agraphical user interface (GUI) based on the first statistical attributeand the first plurality of designs to graphically depict the firststatistical attribute.

2. The computer-implemented method of clause 1, wherein the firststatistical attribute comprises a first statistical dependency betweenthe first design variable and a second design variable that is includedin the first set of design variables.

3. The computer-implemented method of any of clauses 1-2, furthercomprising generating a first GUI element within the GUI that couples afirst portion of the GUI associated with the first design variable to asecond portion of the GUI associated with the second design variable,wherein the GUI graphically depicts the first statistical attribute viathe first GUI element.

4. The computer-implemented method of any of clauses 1-3, furthercomprising generating a first GUI element within the GUI that includesone or more visual attributes that correspond to a magnitude of thefirst statistical dependency.

5. The computer-implemented method of any of clauses 1-4, wherein theGUI comprises a first arc associated with the first design variable anda second arc associated with a second design variable included in thefirst set of design variables, and further comprising generating a firstGUI element within the GUI that couples the first arc to the second arcbased on the first statistical dependency.

6. The computer-implemented method of any of clauses 1-5, wherein theGUI includes a first GUI element that is coupled to a first portion ofthe problem definition that is associated with the first design variableand includes a second GUI element that is coupled to a second portion ofthe problem definition that is associated with the second designvariable, wherein the GUI graphically depicts the first statisticalattribute via the first GUI element.

7. The computer-implemented method of any of clauses 1-6, wherein thefirst statistical attribute comprises a first probability of the firstdesign variable being assigned a first design value in the firstplurality of designs.

8. The computer-implemented method of any of clauses 1-7, furthercomprising generating a first GUI element within the GUI with at leastone visual attribute that is derived from the first probability, whereinthe GUI graphically depicts the first statistical attribute via thefirst GUI element.

9. The computer-implemented method of any of clauses 1-8, furthercomprising generating a second plurality of designs based on the firstprobability and the first set of design variables, analyzing the secondplurality of designs statistically to determine a second statisticalattribute associated with the first design variable, and modify the GUIbased on the second statistical attribute and the second plurality ofdesigns to graphically depict the second statistical attribute.

10. The computer-implemented method of any of clauses 1-9, furthercomprising generating a first probability distribution associated with afirst set of design values assigned to the first set of designvariables, extracting one or more samples from the first probabilitydistribution, generating a second plurality of designs based on the oneor more samples, wherein the second plurality of designs includes asecond set of design values assigned to the first set of designvariables, and modifying the GUI based on the second set of designvalues to graphically depict at least one statistical attribute of thesecond set of design values.

11. Some embodiments include a non-transitory computer-readable mediumstoring program instructions that, when executed by a processor, causethe processor to generate designs by performing the steps of generatinga first plurality of designs based on a problem definition associatedwith a design problem and a first set of design variables associatedwith the design problem, analyzing the first plurality of designsstatistically to determine a first statistical attribute associated witha first design variable included in the first set of design variables,and generating a graphical user interface (GUI) based on the firststatistical attribute and the first plurality of designs to graphicallydepict the first statistical attribute.

12. The non-transitory computer-readable medium of clause 11, whereinthe first statistical attribute comprises a first statistical dependencybetween the first design variable and a second design variable that isincluded in the first set of design variables.

13. The non-transitory computer-readable medium of any of clauses 11-12,further comprising the step of generating a first GUI element within theGUI that couples a first portion of the GUI associated with the firstdesign variable to a second portion of the GUI associated with thesecond design variable, wherein the GUI graphically depicts the firststatistical attribute via the first GUI element.

14. The non-transitory computer-readable medium of any of clauses 11-13,further comprising the step of generating a first GUI element within theGUI that includes one or more visual attributes that correspond to amagnitude of the first statistical dependency.

15. The non-transitory computer-readable medium of any of clauses 11-14,wherein the GUI comprises a first arc associated with the first designvariable and a second arc associated with a second design variableincluded in the first set of design variables, and further comprisingthe step of generating a first GUI element within the GUI that couplesthe first arc to the second arc based on the first statisticaldependency.

16. The non-transitory computer-readable medium of any of clauses 11-15,wherein the GUI includes a first GUI element that is coupled to a firstportion of the problem definition that is associated with the firstdesign variable and includes a second GUI element that is coupled to asecond portion of the problem definition that is associated with thesecond design variable, wherein the GUI graphically depicts the firststatistical attribute via the first GUI element.

17. The non-transitory computer-readable medium of any of clauses 11-16,wherein the first statistical attribute comprises a first probability ofthe first design variable being assigned a first design value in thefirst plurality of designs, and further comprising the step ofgenerating a first GUI element within the GUI with at least one visualattribute that is derived from the first probability, wherein the GUIgraphically depicts the first statistical attribute via the first GUIelement.

18. The non-transitory computer-readable medium of any of clauses 11-17,further comprising the steps of generating a second plurality of designsbased on the first probability and the first set of design variables,analyzing the second plurality of designs statistically to determine asecond statistical attribute associated with the first design variable,and modify the GUI based on the second statistical attribute and thesecond plurality of designs to graphically depict the second statisticalattribute.

19. The non-transitory computer-readable medium of any of clauses 11-18,further comprising the steps of generating a first probabilitydistribution associated with a first set of design values assigned tothe first set of design variables, extracting one or more samples fromthe first probability distribution, generating a second plurality ofdesigns based on the one or more samples, wherein the second pluralityof designs includes a second set of design values assigned to the firstset of design variables, and modifying the GUI based on the second setof design values to graphically depict at least one statisticalattribute of the second set of design values.

20. Some embodiments include a system, comprising a memory storing asoftware application, and a processor that, when executing the softwareapplication, is configured to perform the steps of generating a firstplurality of designs based on a problem definition associated with adesign problem and a first set of design variables associated with thedesign problem, analyzing the first plurality of designs statisticallyto determine a first statistical attribute associated with a firstdesign variable included in the first set of design variables, andgenerating a graphical user interface (GUI) based on the firststatistical attribute and the first plurality of designs to graphicallydepict the first statistical attribute.

Any and all combinations of any of the claim elements recited in any ofthe claims and/or any elements described in this application, in anyfashion, fall within the contemplated scope of the present invention andprotection.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module,” a“system,” or a “computer.” Furthermore, aspects of the presentdisclosure may take the form of a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine. The instructions, when executed via the processor ofthe computer or other programmable data processing apparatus, enable theimplementation of the functions/acts specified in the flowchart and/orblock diagram block or blocks. Such processors may be, withoutlimitation, general purpose processors, special-purpose processors,application-specific processors, or field-programmable gate arrays.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A computer-implemented method for generatingdesigns, the method comprising: generating a first plurality of designsbased on a problem definition associated with a design problem and afirst set of design variables associated with the design problem;analyzing the first plurality of designs to determine a firststatistical dependency between a first design variable included in thefirst set of design variables and a second design variable included inthe first set of design variables; and generating a graphical userinterface (GUI) based on the first plurality of designs to graphicallydepict the first statistical dependency.
 2. The computer-implementedmethod of claim 1, further comprising generating a first GUI elementwithin the GUI that couples a first portion of the GUI associated withthe first design variable to a second portion of the GUI associated withthe second design variable, wherein the GUI graphically depicts thefirst statistical dependency via the first GUI element.
 3. Thecomputer-implemented method of claim 1, further comprising generating afirst GUI element within the GUI that includes one or more visualattributes that correspond to a magnitude of the first statisticaldependency.
 4. The computer-implemented method of claim 1, wherein theGUI comprises a first arc associated with the first design variable anda second arc associated with the second design variable, and furthercomprising generating a first GUI element within the GUI that couplesthe first arc to the second arc based on the first statisticaldependency.
 5. The computer-implemented method of claim 1, wherein theGUI includes a first GUI element that is coupled to a first portion ofthe problem definition that is associated with the first design variableand includes a second GUI element that is coupled to a second portion ofthe problem definition that is associated with the second designvariable, wherein the GUI graphically depicts the first statisticaldependency via the first GUI element.
 6. The computer-implemented methodof claim 1, further comprising generating a probability model thatincludes a set of conditional probability values associated with thefirst design variable and the second design variable.
 7. Thecomputer-implemented method of claim 6, wherein the set of conditionalprobability values includes a different conditional probability valuefor each different combination of design values assignable to the firstdesign variable and the second design variable.
 8. Thecomputer-implemented method of claim 6, wherein the probability modelcomprises a matrix, and a given cell included in the matrix stores aconditional probability value that indicates a probability of anassociated child design variable being assigned a specific valueconditioned on a probability of an associated parent design variablebeing assigned a specific value.
 9. The computer-implemented method ofclaim 6, further comprising generating a design evolution GUI thatanimates how probabilities associated with different design elementsbeing included in generated designs change over successive populationsof generated designs.
 10. The computer-implemented method of claim 1,further comprising: generating a first probability distributionassociated with a first set of design values assigned to the first setof design variables; extracting one or more samples from the firstprobability distribution; generating a second plurality of designs basedon the one or more samples, wherein the second plurality of designsincludes a second set of design values assigned to the first set ofdesign variables; and modifying the GUI based on the second set ofdesign values to graphically depict at least one statistical dependencyassociated with the second set of design values.
 11. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by one or more processors, cause the one or more processors toperform the steps of: generating a first plurality of designs based on aproblem definition associated with a design problem and a first set ofdesign variables associated with the design problem; analyzing the firstplurality of designs to determine a first statistical dependency betweena first design variable included in the first set of design variablesand a second design variable included in the first set of designvariables; and generating a graphical user interface (GUI) based on thefirst plurality of designs to graphically depict the first statisticaldependency.
 12. The one or more non-transitory computer-readable mediaof claim 11, further comprising generating a first GUI element withinthe GUI that couples a first portion of the GUI associated with thefirst design variable to a second portion of the GUI associated with thesecond design variable, wherein the GUI graphically depicts the firststatistical dependency via the first GUI element.
 13. The one or morenon-transitory computer-readable media of claim 11, further comprisinggenerating a first GUI element within the GUI that includes one or morevisual attributes that correspond to a magnitude of the firststatistical dependency.
 14. The one or more non-transitorycomputer-readable media of claim 11, wherein the GUI comprises a firstarc associated with the first design variable and a second arcassociated with the second design variable, and further comprisinggenerating a first GUI element within the GUI that couples the first arcto the second arc based on the first statistical dependency.
 15. The oneor more non-transitory computer-readable media of claim 11, wherein theGUI includes a first GUI element that is coupled to a first portion ofthe problem definition that is associated with the first design variableand includes a second GUI element that is coupled to a second portion ofthe problem definition that is associated with the second designvariable, wherein the GUI graphically depicts the first statisticaldependency via the first GUI element.
 16. The one or more non-transitorycomputer-readable media of claim 11, further comprising generating aprobability model that includes a set of conditional probability valuesassociated with the first design variable and the second designvariable.
 17. The one or more non-transitory computer-readable media ofclaim 16, wherein the set of conditional probability values includes adifferent conditional probability value for each different combinationof design values assignable to the first design variable and the seconddesign variable.
 18. The one or more non-transitory computer-readablemedia of claim 16, wherein the probability model comprises a matrix, anda given cell included in the matrix stores a conditional probabilityvalue that indicates a probability of an associated child designvariable being assigned a specific value conditioned on a probability ofan associated parent design variable being assigned a specific value.19. The one or more non-transitory computer-readable media of claim 16,further comprising generating a design evolution GUI that animates howprobabilities associated with different design elements being includedin generated designs change over successive populations of generateddesigns.
 20. The one or more non-transitory computer-readable media ofclaim 11, further comprising: generating a first probabilitydistribution associated with a first set of design values assigned tothe first set of design variables; extracting one or more samples fromthe first probability distribution; generating a second plurality ofdesigns based on the one or more samples, wherein the second pluralityof designs includes a second set of design values assigned to the firstset of design variables; and modifying the GUI based on the second setof design values to graphically depict at least one statisticaldependency associated with the second set of design values.
 21. Asystem, comprising: one or more memories storing instructions; and oneor more processors that are coupled to the one or more memories and,when executing the instructions, are configured to perform the steps of:generating a first plurality of designs based on a problem definitionassociated with a design problem and a first set of design variablesassociated with the design problem; analyzing the first plurality ofdesigns to determine a first statistical dependency between a firstdesign variable included in the first set of design variables and asecond design variable included in the first set of design variables;and generating a graphical user interface (GUI) based on the firstplurality of designs to graphically depict the first statisticaldependency.